Metasys
Pay not stated in the advert
Overview: Building Autonomous AI Agents
The AI/ML Engineer with an AI Agent Focus is a specialized role dedicated to designing, building, and deploying autonomous AI agents that handle complex business processes independently. You will be at the forefront of integrating cutting-edge Large Language Models (LLMs) and reasoning frameworks into our e-commerce and supply chain ecosystem, driving efficiency and innovation through intelligent automation.
Internship Details
Duration: 3 months
Start Date: Immediate
Location: Remote
Stipend: None initially. Based on your first-quarter performance, you may be offered a paid full-time opportunity, or even be absorbed directly by the client as an FTE.
Key Responsibilities & Core Projects
You will be responsible for the full lifecycle of agent development, from initial design to production optimization and monitoring.
Agent Architecture & Development: Design and build autonomous AI agent architectures utilizing LLMs, advanced reasoning frameworks, and decision-making systems to achieve specific business goals (e.g., customer support automation, sales assistance, workflow optimization).
Business Process Automation: Create agents that integrate directly into the platform to automate critical supply chain and administrative tasks, such including:
Customer Support Agents integrated into the e-commerce storefront.
Internal Workflow Automation agents (e.g., Slack integration, Jira automation).
Multi-Agent Systems: Implement and orchestrate multi-agent systems where specialized agents collaborate to solve complex, multi-step problems across different modules (MES, WMS, OMS).
Advanced Agent Capabilities: Develop and implement memory systems, sophisticated context management, and tool integration strategies (allowing agents to use internal APIs, search, or code execution) to enhance agent efficacy.
Model Optimization: Implement techniques for agent improvement, including fine-tuning of LLM models and applying concepts from Reinforcement Learning for optimal decision-making.
Safety & Monitoring: Implement safety guardrails, define ethical usage guidelines, and build robust monitoring systems (collaborating with MLOps) to track and analyze agent behavior and performance in production.
Required Technologies & Tools
Candidates must possess deep experience in machine learning, LLM technology, and production deployment:
LLM/Agent Frameworks: Mandatory hands-on expertise with agent construction frameworks like LangChain, LlamaIndex, and AutoGen.
Programming & ML: Expert proficiency in Python and standard ML/Deep Learning libraries (e.g., PyTorch, TensorFlow).
Data & APIs: Experience preparing data for model fine-tuning and integrating agents via API endpoints into production services (Node.js/NestJS).
Deployment: Familiarity with MLOps concepts (versioning, deployment) and containerization (Docker) for agent deployment.
Databases: Understanding of vector databases and retrieval augmented generation (RAG) techniques.
Supply Chain Integration (Domain Focus)
You will integrate intelligent agents directly into our core supply chain and data systems.
Data Analysis Agents: Build agents capable of complex data analysis and insight generation using aggregated data from MES, WMS, and OMS.
E-commerce Integration: Deploy agents that assist users directly within the e-commerce platform by accessing real-time catalog and inventory data.
Success Metrics & Career Path
Performance will be measured by:
Agent Autonomy: Measurable increase in the percentage of business tasks handled end-to-end by autonomous agents.
Business Impact: Quantifiable improvements in metrics driven by the agents (e.g., reduced customer support response time, increased sales conversion).
Model Performance: Accuracy and efficiency of fine-tuned models and the reliability of multi-agent systems.
Mentorship Structure: Reports to the Head of Technology/CTO, working closely with the Data Architect and MLOps Engineer to productionize and scale agent capabilities.
Originally posted on Himalayas
